update

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_1_5.py

@@ -9,10 +9,10 @@ import numpy as np
 N = 1000
 
 # Standard deviation of x1
-s1 = 2
+s1 = 10
 
 # Standard deviation of x2
-s2 = 3
+s2 = 10
 
 # Correlation between x1 and x2
 corr = 0.5
@@ -21,7 +21,7 @@ corr = 0.5
 S = np.matrix([[s1*s1, corr*s1*s2], [corr*s1*s2, s2*s2]])
 
 # Mean
-mu = np.array([13, 17])
+mu = np.array([5, 5])
 
 # Number of bins in histogram
 nbins = 20

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_1_6.py

@@ -5,13 +5,12 @@ from matplotlib.pyplot import (figure, subplot, title, imshow, xticks, yticks,
 import scipy.linalg as linalg
 from scipy.io import loadmat
 import numpy as np
-
 # Digits to include in analysis (to include all: n = range(10))
-n = [0]
+n = [9]
 
 # Load Matlab data file to python dict structure
 # and extract variables of interest
-traindata = loadmat('../Data/zipdata.mat')['traindata']
+traindata = loadmat('Software toolboxes/02450ToolBox_Python/Data/zipdata.mat')['traindata']
 X = traindata[:,1:]
 y = traindata[:,0]
 N, M = X.shape

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_1_7.py

@@ -13,7 +13,7 @@ ngen = 10
 
 # Load Matlab data file to python dict structure
 # and extract variables of interest
-traindata = loadmat('../Data/zipdata.mat')['traindata']
+traindata = loadmat('Software toolboxes/02450ToolBox_Python/Data/zipdata.mat')['traindata']
 X = traindata[:,1:]
 y = traindata[:,0]
 N, M = np.shape(X) #or X.shape

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_1.py

@@ -4,7 +4,7 @@ import numpy as np
 import xlrd
 
 # Load xls sheet with data
-doc = xlrd.open_workbook('../Data/iris.xls').sheet_by_index(0)
+doc = xlrd.open_workbook('Software toolboxes/02450ToolBox_Python/Data/iris.xls').sheet_by_index(0)
 
 # Extract attribute names
 attributeNames = doc.row_values(0,0,4)

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_2.py

@@ -3,6 +3,8 @@
 from matplotlib.pyplot import figure, subplot, hist, xlabel, ylim, show
 import numpy as np
 # requires data from exercise 4.2.1
+import sys
+sys.path.append('Software toolboxes/02450ToolBox_Python/Scripts/')
 from ex4_2_1 import *
 
 figure(figsize=(8,7))

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_3.py

@@ -3,6 +3,8 @@
 from matplotlib.pyplot import boxplot, xticks, ylabel, title, show
 
 # requires data from exercise 4.2.1
+import sys
+sys.path.append('Software toolboxes/02450ToolBox_Python/Scripts/')
 from ex4_2_1 import *
 
 boxplot(X)

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_4.py

@@ -2,6 +2,8 @@
 from matplotlib.pyplot import (figure, subplot, boxplot, title, xticks, ylim, 
                                show)
 # requires data from exercise 4.1.1
+import sys
+sys.path.append('Software toolboxes/02450ToolBox_Python/Scripts/')
 from ex4_2_1 import *
 
 figure(figsize=(14,7))

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_5.py

@@ -4,6 +4,8 @@ from matplotlib.pyplot import (figure, subplot, plot, xlabel, ylabel,
                                xticks, yticks,legend,show)
 
 # requires data from exercise 4.2.1
+import sys
+sys.path.append('Software toolboxes/02450ToolBox_Python/Scripts/')
 from ex4_2_1 import *
 
 figure(figsize=(12,10))

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_6.py

 # Exercise 4.2.6
 
-from matplotlib.pyplot import (figure, show, hold)
+from matplotlib.pyplot import (figure, show)
 from mpl_toolkits.mplot3d import Axes3D
 
 # requires data from exercise 4.1.1
+import sys
+sys.path.append('Software toolboxes/02450ToolBox_Python/Scripts/')
 from ex4_2_1 import *
 
 # Indices of the variables to plot

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_2_7.py

@@ -5,6 +5,8 @@ from matplotlib.pyplot import (figure, imshow, xticks, xlabel, ylabel, title,
 from scipy.stats import zscore
 
 # requires data from exercise 4.2.1
+import sys
+sys.path.append('Software toolboxes/02450ToolBox_Python/Scripts/')
 from ex4_2_1 import *
 
 X_standarized = zscore(X, ddof=1)

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_3_1.py

@@ -7,7 +7,7 @@ from scipy.io import loadmat
 from scipy.stats import zscore
 
 # Load Matlab data file and extract variables of interest
-mat_data = loadmat('../Data/wine.mat')
+mat_data = loadmat('Software toolboxes/02450ToolBox_Python/Data/wine.mat')
 X = mat_data['X']
 y = mat_data['y'].squeeze()
 C = mat_data['C'][0,0]

02450/Software toolboxes/02450Toolbox_Python/Scripts/ex4_3_2.py

 # exercise 4.3.2
 
-from matplotlib.pyplot import (figure, hold, subplot, plot, legend, show, 
+from matplotlib.pyplot import (figure, subplot, plot, legend, show, 
                                xlabel, ylabel, xticks, yticks, boxplot, setp,title,ylim)
 import numpy as np
 from scipy.io import loadmat
 from scipy.stats import zscore
 
 # Load Matlab data file and extract variables of interest
-mat_data = loadmat('../Data/wine.mat')
+mat_data = loadmat('Software toolboxes/02450ToolBox_Python/Data/wine.mat')
 X = mat_data['X']
 y = np.squeeze(mat_data['y'])
 C = mat_data['C'][0,0]